# Theatre Migrants

To generate a knowledge graph about migrants in the theatre in Europe.

## Running the scripts

The mapping scripts have been reimplemented in Rust for faster execution. Both
scripts must be run from this directory (`mapping/`).

**Prerequisites:** Start the MariaDB container before running step 1:

```sh
docker compose up -d
```

**Step 1** — Direct Mapping from MariaDB to RDF (`data/graph-01.ttl`):

```sh
cargo run --release --bin step-01
```

**Step 2** — Apply SPARQL UPDATE queries (`data/graph-02.ttl`):

```sh
cargo run --release --bin step-02
```

Alternatively, after installing with `cargo install --path .`:

```sh
step-01
step-02
```

## Generating the ontology

Next there are set of steps describing how to generate the migrants RDF graph.

### Step 1 - Loading the input data into a relational database

#### Task

The file `teatre-migrants.sql` contains the dump of a MariaDB database. The tables involved in this schema are described in the file `db_schema.md`. We will load this data in MariaDB to access the data with SQL. To this end:

1. Create a Dockerfile to create a docker container for MariaDB.

2. Upload the dump into a database in the container.

3. Create a Rust program `src/map/step_01.rs` that connects to the database. This program should return a file called `graph-01.ttl` containing all the data from the tables loaded in the database using the direct mapping from relational databases to RDF.

#### Summary

The `Dockerfile` creates a MariaDB 10.11 container that automatically loads `teatre-migrants.sql` on first start. The `docker-compose.yml` exposes the database on port 3306 with a healthcheck.

The program `src/map/step_01.rs` connects to the database and implements the [W3C Direct Mapping](https://www.w3.org/TR/rdb-direct-mapping/) for all 9 tables (`location`, `migration_table`, `organisation`, `person`, `person_profession`, `personnames`, `relationship`, `religions`, `work`). Each table row becomes an RDF resource identified by its primary key, each column becomes a datatype property, and each foreign key becomes an object property linking to the referenced row. The output file `graph-01.ttl` contains 162,029 triples.

To run:

```sh
docker compose up -d
cargo run --release --bin step-01
```

### Step 2 - Generate Objects

Continents and countries should be objects instead of literals. To this end, we can transform the following data:

```
base:location\/ARG-BahBlanca-00 a base:location;
  base:location\#City "Bahia Blanca";
  base:location\#Continent "South America";
  base:location\#Country "Argentina";
  base:location\#GeoNamesID "3865086";
  base:location\#IDLocation "ARG-BahBlanca-00";
  base:location\#latitude -3.87253e1;
  base:location\#longitude -6.22742e1;
  base:location\#wikidata "Q54108";
  base:location\#wikipedia "https://en.wikipedia.org/wiki/Bah%C3%ADa_Blanca" .
```

Into the following data:

```
base:location\/ARG-BahBlanca-00 a base:location;
  base:location\#City base:City-BahiaBlanca;
  base:location\#Continent base:Continent-SouthAmerica;
  base:location\#Country base:Country-Argentina;
  base:location\#GeoNamesID "3865086";
  base:location\#IDLocation "ARG-BahBlanca-00";
  base:location\#latitude -3.87253e1;
  base:location\#longitude -6.22742e1;
  base:location\#wikidata "Q54108";
  base:location\#wikipedia "https://en.wikipedia.org/wiki/Bah%C3%ADa_Blanca" .

base:City-BahiaBlanca a base:City;
  rdfs:label "Bahia Blanca"@en .

base:Continent-SouthAmerica a base:Continent;
  rdfs:label "South America"@en .

base:Country-Argentina a base:Country;
  rdfs:label "Argentina"@en .
```

Notice that all ranges of property `rdfs:label` are stated to be in English.

Generate an SPARQL UPDATE query that do this tranformation for all elements of the table and save it a new folder called `updates`. Do the same with the other tables, proposing which columns should be defined as objects. For every table define a different SPARQL UPDATE query and to be saved in the `updates` folder. Enumerate these generated queries adding a prefix number like 001, 002, 003, and so on.

After generating the update queries, generate a Rust program that executes the updates on the RDF graph generated in the previous step and generates a new RDF graph to be saved: `data/graph-02.ttl`.

#### Summary

19 SPARQL UPDATE queries in `updates/` transform literal values into typed objects across all tables:

| Query | Table | Column | Object type |
|-------|-------|--------|-------------|
| 001 | location | Continent | Continent |
| 002 | location | Country | Country |
| 003 | location | State | State |
| 004 | location | City | City |
| 005 | migration_table | reason | MigrationReason |
| 006 | migration_table | reason2 | MigrationReason |
| 007 | organisation | InstType | InstitutionType |
| 008 | person | gender | Gender |
| 009 | person | Nametype | Nametype |
| 010 | person | Importsource | ImportSource |
| 011 | person_profession | Eprofession | Profession |
| 012 | personnames | Nametype | Nametype |
| 013 | relationship | Relationshiptype | RelationshipType |
| 014 | relationship | relationshiptype_precise | RelationshipTypePrecise |
| 015 | religions | religion | Religion |
| 016 | work | Profession | Profession |
| 017 | work | Profession2 | Profession |
| 018 | work | Profession3 | Profession |
| 019 | work | EmploymentType | EmploymentType |

Each query replaces a literal value with an object reference and creates the object with `rdf:type` and `rdfs:label` (in English). The program `src/map/step_02.rs` loads `data/graph-01.ttl`, applies all queries in order, and writes `data/graph-02.ttl` (164,632 triples).

To run:

```sh
cargo run --release --bin step-02
```

### Step 3 - Annotate dataypes

In the previous example we have dates like "1894-12-31", which is represented as an `xsd:string` datatype. Please infer the datatypes of these literals and create a new SPARQL query to generate a new RDF graph where literals use these dataypes.

### Step 4 - Replace empty string with unbound values

Intuitively, the triple 

```
work:4 workp:EmploymentType workp:comment "" .
```

does not intended to mean a comment "", but the lack of a comment. So, write a query that exclude these comments from the next generated graph.

### Step 5 - Use well-known vocabularies

For some classes, properties, and individuals we can be represented with Schema.org. For example, the class `migrants:person` can be represented with the class `schema:Person`. Please propose what of these elements could use the Schema.org vocabulary and generate an SPARQL to generate the next graph. Consider using other vocabularies beyond Schema.org, if you consider them appropiate to represent the information on this dataset.
